Overview

Dataset statistics

Number of variables13
Number of observations62628
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 MiB
Average record size in memory145.7 B

Variable types

Numeric12
Categorical1

Alerts

Humidity[%] is highly overall correlated with Pressure[hPa] and 1 other fieldsHigh correlation
TVOC[ppb] is highly overall correlated with Raw EthanolHigh correlation
eCO2[ppm] is highly overall correlated with Raw H2High correlation
Raw H2 is highly overall correlated with eCO2[ppm]High correlation
Raw Ethanol is highly overall correlated with TVOC[ppb] and 1 other fieldsHigh correlation
Pressure[hPa] is highly overall correlated with Humidity[%] and 1 other fieldsHigh correlation
PM1.0 is highly overall correlated with PM2.5 and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with PM1.0 and 3 other fieldsHigh correlation
NC0.5 is highly overall correlated with PM1.0 and 3 other fieldsHigh correlation
NC1.0 is highly overall correlated with PM1.0 and 3 other fieldsHigh correlation
NC2.5 is highly overall correlated with PM1.0 and 3 other fieldsHigh correlation
Fire Alarm is highly overall correlated with Humidity[%] and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-05-09 00:03:57.036086
Analysis finished2023-05-09 00:04:17.836975
Duration20.8 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Temperature[C]
Real number (ℝ)

Distinct21672
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7507808 × 10-15
Minimum-2.6449125
Maximum3.0613468
Zeros0
Zeros (%)0.0%
Negative22528
Negative (%)36.0%
Memory size3.0 MiB
2023-05-09T00:04:17.914281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6449125
5-th percentile-1.7577752
Q1-0.34653102
median0.28969531
Q30.65720027
95-th percentile0.90321916
Maximum3.0613468
Range5.7062593
Interquartile range (IQR)1.0037313

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1.7388943 × 1014
Kurtosis0.453472
Mean5.7507808 × 10-15
Median Absolute Deviation (MAD)0.4425241
Skewness-0.6198558
Sum3.610694 × 10-10
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:18.425195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5926270603 222
 
0.4%
0.5947162448 206
 
0.3%
0.5919306655 193
 
0.3%
0.7646365854 191
 
0.3%
0.7667257699 189
 
0.3%
0.7695113493 187
 
0.3%
0.5905378758 186
 
0.3%
0.5954126397 183
 
0.3%
0.5975018242 182
 
0.3%
0.7625474009 180
 
0.3%
Other values (21662) 60709
96.9%
ValueCountFrequency (%)
-2.644912543 24
< 0.1%
-2.644842904 12
< 0.1%
-2.644773264 9
 
< 0.1%
-2.644703625 5
 
< 0.1%
-2.644633985 8
 
< 0.1%
-2.644564346 4
 
< 0.1%
-2.644494706 6
 
< 0.1%
-2.644425067 4
 
< 0.1%
-2.644355427 6
 
< 0.1%
-2.644285788 4
 
< 0.1%
ValueCountFrequency (%)
3.061346762 2
< 0.1%
3.058561183 2
< 0.1%
3.056471998 1
< 0.1%
3.055775603 1
< 0.1%
3.051597234 1
< 0.1%
3.050204445 1
< 0.1%
3.048811655 2
< 0.1%
3.046722471 2
< 0.1%
3.044633286 1
< 0.1%
3.041847707 2
< 0.1%

Humidity[%]
Real number (ℝ)

Distinct3890
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1269207 × 10-14
Minimum-4.2636837
Maximum3.0072112
Zeros0
Zeros (%)0.0%
Negative23657
Negative (%)37.8%
Memory size3.0 MiB
2023-05-09T00:04:18.560635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.2636837
5-th percentile-2.7144732
Q1-0.11388189
median0.18164626
Q30.53018901
95-th percentile0.90805897
Maximum3.0072112
Range7.270895
Interquartile range (IQR)0.6440709

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)8.8737389 × 1013
Kurtosis6.9449394
Mean1.1269207 × 10-14
Median Absolute Deviation (MAD)0.32147146
Skewness-2.4607742
Sum7.0503292 × 10-10
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:18.690886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1172658039 130
 
0.2%
-0.1229056542 130
 
0.2%
-0.07665888207 126
 
0.2%
0.5166533651 126
 
0.2%
-0.08793858259 126
 
0.2%
0.5425966763 124
 
0.2%
-0.09921828311 124
 
0.2%
-0.1037301633 123
 
0.2%
-0.1251615943 122
 
0.2%
-0.0947064029 122
 
0.2%
Other values (3880) 61375
98.0%
ValueCountFrequency (%)
-4.263683714 2
< 0.1%
-4.260299804 2
< 0.1%
-4.258043864 2
< 0.1%
-4.256915893 2
< 0.1%
-4.253531983 2
< 0.1%
-4.252404013 2
< 0.1%
-4.251276043 2
< 0.1%
-4.250148073 2
< 0.1%
-4.249020103 2
< 0.1%
-4.244508223 4
< 0.1%
ValueCountFrequency (%)
3.007211239 2
< 0.1%
2.993675599 2
< 0.1%
2.972244168 2
< 0.1%
2.950812737 2
< 0.1%
2.947428826 2
< 0.1%
2.924869425 2
< 0.1%
2.910205815 2
< 0.1%
2.889902354 2
< 0.1%
2.857191222 2
< 0.1%
2.832375881 2
< 0.1%

TVOC[ppb]
Real number (ℝ)

Distinct1966
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4522174 × 10-17
Minimum-0.2486164
Maximum7.4321638
Zeros0
Zeros (%)0.0%
Negative59863
Negative (%)95.6%
Memory size3.0 MiB
2023-05-09T00:04:18.829776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.2486164
5-th percentile-0.24810435
Q1-0.23197471
median-0.12303564
Q3-0.096408938
95-th percentile-0.065429791
Maximum7.4321638
Range7.6807802
Interquartile range (IQR)0.13556577

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)6.8860214 × 1016
Kurtosis45.859504
Mean1.4522174 × 10-17
Median Absolute Deviation (MAD)0.053893475
Skewness6.7767912
Sum-1.5586421 × 10-12
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:18.959719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2486164 2698
 
4.3%
7.432163841 928
 
1.5%
-0.1025535624 320
 
0.5%
-0.1031936274 264
 
0.4%
-0.1017854844 258
 
0.4%
-0.1029376014 256
 
0.4%
-0.1026815754 256
 
0.4%
-0.2472082569 255
 
0.4%
-0.1015294584 252
 
0.4%
-0.1022975364 250
 
0.4%
Other values (1956) 56891
90.8%
ValueCountFrequency (%)
-0.2486164 2698
4.3%
-0.248488387 143
 
0.2%
-0.248360374 121
 
0.2%
-0.248232361 126
 
0.2%
-0.248104348 113
 
0.2%
-0.247976335 130
 
0.2%
-0.247848322 158
 
0.3%
-0.247720309 150
 
0.2%
-0.247592296 141
 
0.2%
-0.247464283 147
 
0.2%
ValueCountFrequency (%)
7.432163841 928
1.5%
7.428195437 2
 
< 0.1%
7.368925417 2
 
< 0.1%
7.206604928 2
 
< 0.1%
7.1393981 2
 
< 0.1%
7.036603658 2
 
< 0.1%
6.874283169 2
 
< 0.1%
6.799139536 2
 
< 0.1%
6.755615114 2
 
< 0.1%
6.696345094 2
 
< 0.1%

eCO2[ppm]
Real number (ℝ)

Distinct1713
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4522174 × 10-17
Minimum-0.14167956
Maximum31.129384
Zeros0
Zeros (%)0.0%
Negative58062
Negative (%)92.7%
Memory size3.0 MiB
2023-05-09T00:04:19.098807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.14167956
5-th percentile-0.14167956
Q1-0.14167956
median-0.14167956
Q3-0.12174163
95-th percentile0.027792821
Maximum31.129384
Range31.271064
Interquartile range (IQR)0.019937927

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)6.8860214 × 1016
Kurtosis204.56004
Mean1.4522174 × 10-17
Median Absolute Deviation (MAD)0
Skewness12.459611
Sum4.8072657 × 10-12
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:19.229866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1416795559 31922
51.0%
-0.1395808268 942
 
1.5%
-0.1411548736 925
 
1.5%
-0.1385314622 918
 
1.5%
-0.1374820976 896
 
1.4%
-0.1364327331 858
 
1.4%
-0.1348586862 759
 
1.2%
-0.1338093217 613
 
1.0%
-0.1327599571 532
 
0.8%
-0.1311859102 510
 
0.8%
Other values (1703) 23753
37.9%
ValueCountFrequency (%)
-0.1416795559 31922
51.0%
-0.1411548736 925
 
1.5%
-0.1406301913 484
 
0.8%
-0.140105509 484
 
0.8%
-0.1395808268 942
 
1.5%
-0.1390561445 508
 
0.8%
-0.1385314622 918
 
1.5%
-0.1380067799 400
 
0.6%
-0.1374820976 896
 
1.4%
-0.1369574154 418
 
0.7%
ValueCountFrequency (%)
31.12938447 2
< 0.1%
29.83184518 1
< 0.1%
27.17118133 1
< 0.1%
26.86686561 1
< 0.1%
26.64177691 1
< 0.1%
23.50417686 1
< 0.1%
23.25285405 1
< 0.1%
22.78955959 1
< 0.1%
21.75698486 1
< 0.1%
21.59800613 1
< 0.1%

Raw H2
Real number (ℝ)

Distinct1830
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.3897565 × 10-16
Minimum-8.3476116
Maximum3.1583257
Zeros0
Zeros (%)0.0%
Negative32987
Negative (%)52.7%
Memory size3.0 MiB
2023-05-09T00:04:19.366058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-8.3476116
5-th percentile-0.81149793
Q1-0.41273555
median-0.067740938
Q30.61123781
95-th percentile1.1507507
Maximum3.1583257
Range11.505937
Interquartile range (IQR)1.0239734

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-1.5650049 × 1015
Kurtosis18.978487
Mean-6.3897565 × 10-16
Median Absolute Deviation (MAD)0.44775896
Skewness-2.9100728
Sum-4.0214498 × 10-11
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:19.500422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09741605592 328
 
0.5%
-0.599913473 319
 
0.5%
-0.5742223851 316
 
0.5%
-0.5632119189 310
 
0.5%
-0.5815626959 310
 
0.5%
-0.555871608 306
 
0.5%
0.1047563668 292
 
0.5%
-0.5889030068 289
 
0.5%
-0.5925731622 286
 
0.5%
0.1084265222 282
 
0.5%
Other values (1820) 59590
95.1%
ValueCountFrequency (%)
-8.347611555 2
< 0.1%
-8.307239845 2
< 0.1%
-8.292559224 2
< 0.1%
-8.193465028 2
< 0.1%
-8.171444095 2
< 0.1%
-7.940224304 2
< 0.1%
-7.929213838 2
< 0.1%
-7.921873527 2
< 0.1%
-7.918203371 2
< 0.1%
-7.892512284 2
< 0.1%
ValueCountFrequency (%)
3.158325672 2
< 0.1%
3.150985362 2
< 0.1%
3.147315206 2
< 0.1%
3.143645051 2
< 0.1%
3.128964429 2
< 0.1%
3.121624118 2
< 0.1%
3.106943497 2
< 0.1%
3.092262875 2
< 0.1%
3.084922564 2
< 0.1%
3.070241942 2
< 0.1%

Raw Ethanol
Real number (ℝ)

Distinct2659
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6967521 × 10-16
Minimum-7.280139
Maximum2.7166207
Zeros0
Zeros (%)0.0%
Negative34967
Negative (%)55.8%
Memory size3.0 MiB
2023-05-09T00:04:19.638082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-7.280139
5-th percentile-0.61399182
Q1-0.52375355
median-0.41546763
Q30.53121386
95-th percentile1.5134164
Maximum2.7166207
Range9.9967597
Interquartile range (IQR)1.0549674

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1.2992493 × 1015
Kurtosis11.092994
Mean7.6967521 × 10-16
Median Absolute Deviation (MAD)0.20672767
Skewness-1.6847822
Sum4.8012705 × 10-11
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:19.767755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5188314659 685
 
1.1%
-0.5106279868 504
 
0.8%
-0.5024245076 494
 
0.8%
-0.5057058993 486
 
0.8%
-0.5122686826 479
 
0.8%
-0.499143116 464
 
0.7%
-0.5139093784 462
 
0.7%
-0.5073465951 459
 
0.7%
-0.5171907701 458
 
0.7%
-0.5040652035 458
 
0.7%
Other values (2649) 57679
92.1%
ValueCountFrequency (%)
-7.280138962 2
< 0.1%
-7.196463475 2
< 0.1%
-7.137398425 2
< 0.1%
-7.058645026 2
< 0.1%
-7.029112501 2
< 0.1%
-7.027471805 2
< 0.1%
-7.025831109 2
< 0.1%
-6.986454409 2
< 0.1%
-6.951999797 2
< 0.1%
-6.948718405 2
< 0.1%
ValueCountFrequency (%)
2.716620702 4
< 0.1%
2.703495135 2
< 0.1%
2.701854439 4
< 0.1%
2.69365096 2
< 0.1%
2.690369569 2
< 0.1%
2.683806785 2
< 0.1%
2.682166089 2
< 0.1%
2.677244002 2
< 0.1%
2.675603306 4
< 0.1%
2.67396261 2
< 0.1%

Pressure[hPa]
Real number (ℝ)

Distinct2213
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0037841 × 10-12
Minimum-5.840443
Maximum0.9263646
Zeros0
Zeros (%)0.0%
Negative12640
Negative (%)20.2%
Memory size3.0 MiB
2023-05-09T00:04:19.900146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.840443
5-th percentile-1.335998
Q10.05431846
median0.14144796
Q30.59362003
95-th percentile0.81970607
Maximum0.9263646
Range6.7668076
Interquartile range (IQR)0.53930157

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)4.9905577 × 1011
Kurtosis17.484463
Mean2.0037841 × 10-12
Median Absolute Deviation (MAD)0.29969544
Skewness-3.6041068
Sum1.2549285 × 10-7
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:20.030997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06107850709 304
 
0.5%
0.05882515791 284
 
0.5%
0.06633632187 278
 
0.4%
0.06258073989 266
 
0.4%
0.06182962349 266
 
0.4%
0.06408297268 262
 
0.4%
0.05657180872 260
 
0.4%
0.06934078745 256
 
0.4%
0.05807404151 252
 
0.4%
0.06708743826 252
 
0.4%
Other values (2203) 59948
95.7%
ValueCountFrequency (%)
-5.840443018 3
< 0.1%
-5.836687436 1
 
< 0.1%
-5.83593632 1
 
< 0.1%
-5.83368297 2
< 0.1%
-5.832931854 1
 
< 0.1%
-5.832180738 2
< 0.1%
-5.831429621 2
< 0.1%
-5.829927388 2
< 0.1%
-5.829176272 3
< 0.1%
-5.828425156 1
 
< 0.1%
ValueCountFrequency (%)
0.9263645955 2
 
< 0.1%
0.9226090135 6
< 0.1%
0.919604548 6
< 0.1%
0.9188534316 4
 
< 0.1%
0.9181023152 10
< 0.1%
0.9173511988 2
 
< 0.1%
0.9166000824 8
< 0.1%
0.915848966 8
< 0.1%
0.9150978496 4
 
< 0.1%
0.9143467332 8
< 0.1%

PM1.0
Real number (ℝ)

Distinct1337
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.3566521 × 10-17
Minimum-0.10904414
Maximum15.428178
Zeros0
Zeros (%)0.0%
Negative61464
Negative (%)98.1%
Memory size3.0 MiB
2023-05-09T00:04:20.171000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.10904414
5-th percentile-0.10871895
Q1-0.10765667
median-0.10708217
Q3-0.10677866
95-th percentile-0.10626919
Maximum15.428178
Range15.537222
Interquartile range (IQR)0.00087801184

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-2.2953405 × 1016
Kurtosis122.75661
Mean-4.3566521 × 10-17
Median Absolute Deviation (MAD)0.00035770853
Skewness10.75249
Sum-2.8759495 × 10-12
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:20.304237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1071038464 680
 
1.1%
-0.107147205 655
 
1.0%
-0.1070388085 641
 
1.0%
-0.1069737705 637
 
1.0%
-0.1071255257 631
 
1.0%
-0.1071363653 608
 
1.0%
-0.1070496481 601
 
1.0%
-0.1069195723 600
 
1.0%
-0.1069304119 591
 
0.9%
-0.107114686 586
 
0.9%
Other values (1327) 56398
90.1%
ValueCountFrequency (%)
-0.1090441441 140
0.2%
-0.1090333045 48
 
0.1%
-0.1090224648 30
 
< 0.1%
-0.1090116252 24
 
< 0.1%
-0.1090007855 26
 
< 0.1%
-0.1089899459 40
 
0.1%
-0.1089791062 88
0.1%
-0.1089682666 44
 
0.1%
-0.1089574269 42
 
0.1%
-0.1089465873 70
0.1%
ValueCountFrequency (%)
15.42817759 1
< 0.1%
15.41135445 1
< 0.1%
15.39072659 1
< 0.1%
15.28969019 1
< 0.1%
15.22803425 1
< 0.1%
15.14578296 1
< 0.1%
15.00764243 1
< 0.1%
14.91374936 1
< 0.1%
14.79394952 1
< 0.1%
14.76734902 1
< 0.1%

PM2.5
Real number (ℝ)

Distinct1351
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7426609 × 10-16
Minimum-0.093341166
Maximum22.894774
Zeros0
Zeros (%)0.0%
Negative61509
Negative (%)98.2%
Memory size3.0 MiB
2023-05-09T00:04:20.444395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.093341166
5-th percentile-0.093184311
Q1-0.092663144
median-0.092389912
Q3-0.092238116
95-th percentile-0.091995242
Maximum22.894774
Range22.988115
Interquartile range (IQR)0.00042502875

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-5.7383512 × 1015
Kurtosis228.19821
Mean-1.7426609 × 10-16
Median Absolute Deviation (MAD)0.00017709531
Skewness14.145996
Sum-1.144974 × 10-11
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:20.575399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0924000313 640
 
1.0%
-0.09242027077 635
 
1.0%
-0.09231401358 630
 
1.0%
-0.0924152109 613
 
1.0%
-0.0923696721 594
 
0.9%
-0.09233425304 594
 
0.9%
-0.09237473197 591
 
0.9%
-0.09241015103 591
 
0.9%
-0.09236461224 589
 
0.9%
-0.09240509117 574
 
0.9%
Other values (1341) 56577
90.3%
ValueCountFrequency (%)
-0.09334116639 118
0.2%
-0.09333610652 20
 
< 0.1%
-0.09333104665 20
 
< 0.1%
-0.09332598679 20
 
< 0.1%
-0.09332092692 18
 
< 0.1%
-0.09331586706 14
 
< 0.1%
-0.09331080719 16
 
< 0.1%
-0.09330574732 22
 
< 0.1%
-0.09330068746 40
 
0.1%
-0.09329562759 60
0.1%
ValueCountFrequency (%)
22.89477383 1
< 0.1%
22.6853561 1
< 0.1%
22.5679925 1
< 0.1%
22.35116206 1
< 0.1%
22.05119802 1
< 0.1%
21.71209592 1
< 0.1%
21.63335935 1
< 0.1%
21.38579022 1
< 0.1%
21.2805956 1
< 0.1%
21.01392548 1
< 0.1%

NC0.5
Real number (ℝ)

Distinct3093
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1783261 × 10-16
Minimum-0.11521569
Maximum14.297804
Zeros0
Zeros (%)0.0%
Negative61418
Negative (%)98.1%
Memory size3.0 MiB
2023-05-09T00:04:20.708294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.11521569
5-th percentile-0.11474215
Q1-0.11314805
median-0.11229708
Q3-0.11183526
95-th percentile-0.11108744
Maximum14.297804
Range14.41302
Interquartile range (IQR)0.0013127887

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)4.5906809 × 1015
Kurtosis105.73883
Mean2.1783261 × 10-16
Median Absolute Deviation (MAD)0.00053918106
Skewness10.041158
Sum1.4185098 × 10-11
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:20.839055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1152156872 182
 
0.3%
-0.1121282896 118
 
0.2%
-0.1122220602 116
 
0.2%
-0.1123861588 114
 
0.2%
-0.1123345849 114
 
0.2%
-0.1122455028 114
 
0.2%
-0.1121681421 112
 
0.2%
-0.1123252079 112
 
0.2%
-0.1123697489 111
 
0.2%
-0.1123509948 110
 
0.2%
Other values (3083) 61425
98.1%
ValueCountFrequency (%)
-0.1152156872 182
0.3%
-0.1152133429 58
 
0.1%
-0.1152109986 26
 
< 0.1%
-0.1152086544 16
 
< 0.1%
-0.1152063101 14
 
< 0.1%
-0.1152039659 16
 
< 0.1%
-0.1152016216 10
 
< 0.1%
-0.1151992773 10
 
< 0.1%
-0.1151969331 4
 
< 0.1%
-0.1151945888 4
 
< 0.1%
ValueCountFrequency (%)
14.29780423 2
< 0.1%
14.28497876 2
< 0.1%
14.05416003 1
< 0.1%
13.78655744 1
< 0.1%
13.67020217 2
< 0.1%
13.61770469 1
< 0.1%
13.5098005 2
< 0.1%
13.50677405 2
< 0.1%
13.42946486 2
< 0.1%
13.41151951 2
< 0.1%

NC1.0
Real number (ℝ)

Distinct4113
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-0.091924933
Maximum23.348249
Zeros0
Zeros (%)0.0%
Negative61518
Negative (%)98.2%
Memory size3.0 MiB
2023-05-09T00:04:20.976782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.091924933
5-th percentile-0.091778643
Q1-0.091300038
median-0.091047642
Q3-0.090909479
95-th percentile-0.090684174
Maximum23.348249
Range23.440174
Interquartile range (IQR)0.0003905591

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)nan
Kurtosis238.34805
Mean0
Median Absolute Deviation (MAD)0.0001616418
Skewness14.447263
Sum-1.1617374 × 10-12
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:21.106553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.09103274243 84
 
0.1%
-0.09106479988 84
 
0.1%
-0.09102596973 82
 
0.1%
-0.09102416367 82
 
0.1%
-0.09192493293 80
 
0.1%
-0.09106163929 80
 
0.1%
-0.09105667264 80
 
0.1%
-0.09097901233 80
 
0.1%
-0.09106976653 80
 
0.1%
-0.09104267572 80
 
0.1%
Other values (4103) 61816
98.7%
ValueCountFrequency (%)
-0.09192493293 80
0.1%
-0.09192448142 16
 
< 0.1%
-0.09192402991 8
 
< 0.1%
-0.09192357839 6
 
< 0.1%
-0.09192312688 4
 
< 0.1%
-0.09192267537 4
 
< 0.1%
-0.09192222385 2
 
< 0.1%
-0.09192177234 2
 
< 0.1%
-0.09192132083 2
 
< 0.1%
-0.09192086931 2
 
< 0.1%
ValueCountFrequency (%)
23.34824943 1
< 0.1%
23.12211887 1
< 0.1%
23.01098291 1
< 0.1%
22.76664688 1
< 0.1%
22.45509766 1
< 0.1%
22.10334929 1
< 0.1%
22.0460107 1
< 0.1%
21.77147971 1
< 0.1%
21.65259081 1
< 0.1%
21.38307071 1
< 0.1%

NC2.5
Real number (ℝ)

Distinct1161
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.4789195 × 10-16
Minimum-0.073889208
Maximum27.641107
Zeros0
Zeros (%)0.0%
Negative61845
Negative (%)98.7%
Memory size3.0 MiB
2023-05-09T00:04:21.237293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.073889208
5-th percentile-0.073881824
Q1-0.073858748
median-0.073848595
Q3-0.073842134
95-th percentile-0.073831058
Maximum27.641107
Range27.714996
Interquartile range (IQR)1.6614356 × 10-5

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-1.3370915 × 1015
Kurtosis356.94191
Mean-7.4789195 × 10-16
Median Absolute Deviation (MAD)7.3841583 × 10-6
Skewness17.736443
Sum-4.7076565 × 10-11
Variance1
MonotonicityNot monotonic
2023-05-09T00:04:21.362211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.07384951819 2611
 
4.2%
-0.07384582611 2446
 
3.9%
-0.07384767215 2428
 
3.9%
-0.07384674913 2402
 
3.8%
-0.07384859517 2345
 
3.7%
-0.07385044121 2264
 
3.6%
-0.07384490309 2201
 
3.5%
-0.07384398007 2111
 
3.4%
-0.07384305705 2071
 
3.3%
-0.07385136423 2001
 
3.2%
Other values (1151) 39748
63.5%
ValueCountFrequency (%)
-0.07388920804 88
 
0.1%
-0.07388828502 18
 
< 0.1%
-0.073887362 30
 
< 0.1%
-0.07388643898 168
 
0.3%
-0.07388551596 284
 
0.5%
-0.07388459294 448
0.7%
-0.07388366992 720
1.1%
-0.0738827469 866
1.4%
-0.07388182388 652
1.0%
-0.07388090086 846
1.4%
ValueCountFrequency (%)
27.64110719 1
< 0.1%
27.23890132 1
< 0.1%
27.1988358 1
< 0.1%
26.66124231 1
< 0.1%
26.23204457 1
< 0.1%
25.92995773 1
< 0.1%
25.75220443 1
< 0.1%
25.36886969 1
< 0.1%
25.1038984 1
< 0.1%
24.81081839 1
< 0.1%

Fire Alarm
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
1
44757 
0
17871 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters62628
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 44757
71.5%
0 17871
 
28.5%

Length

2023-05-09T00:04:21.479098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T00:04:21.580507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 44757
71.5%
0 17871
 
28.5%

Most occurring characters

ValueCountFrequency (%)
1 44757
71.5%
0 17871
 
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62628
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44757
71.5%
0 17871
 
28.5%

Most occurring scripts

ValueCountFrequency (%)
Common 62628
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44757
71.5%
0 17871
 
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44757
71.5%
0 17871
 
28.5%

Interactions

2023-05-09T00:04:16.025182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:03:59.792320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.189489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.647535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.330637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.769145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.191045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.587166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.362096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.844242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.222714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.662577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.138726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:03:59.909066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.310046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.765928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.449027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.885482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.306161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.708669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.484639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.958693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.341532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.774531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.259615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.029751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.435164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.121757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.572785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.007623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.428283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.837180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.612118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.079926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.466096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.893121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.378025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.148658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.560380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.246376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.696429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.128413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.548977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.964242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.739097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.199951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.590468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.010636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.494157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.266253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.683312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.366382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.817697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.248203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.665616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:09.087700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.864569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.315596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.711589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.124925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.607507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.380403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.804026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.484746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.934221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.363968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.779850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:09.209419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.984774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.428623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.829947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.235671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.718893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.491422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.920334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.598895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.049790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.476892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.889604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:09.328839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.103643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.537712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.945910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.344049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.842348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.611420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.046051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.725067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.173815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.598962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.010820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:09.456296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.230908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.657506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.070727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.461995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:16.966916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.734821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.173759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.853046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.300476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.723569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.132988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:09.585722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.359634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.777794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.197050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.581165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:17.078787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.844878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.289421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:03.967903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.414494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.836229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.243457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:09.998844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.476454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:12.886338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.310929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.691362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:17.197159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:00.964377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.414130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.093309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.537760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:06.958098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.363384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.124908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.602813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.003899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.433530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.807455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:17.306127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:01.075332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:02.528946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:04.210883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:05.651180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:07.070300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:08.473275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:10.242886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:11.721529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:13.111341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:14.546681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-09T00:04:15.914899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-05-09T00:04:21.669627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Temperature[C]Humidity[%]TVOC[ppb]eCO2[ppm]Raw H2Raw EthanolPressure[hPa]PM1.0PM2.5NC0.5NC1.0NC2.5Fire Alarm
Temperature[C]1.000-0.2750.0530.165-0.211-0.034-0.2670.0130.0130.0130.0130.0090.396
Humidity[%]-0.2751.000-0.016-0.1040.222-0.1360.556-0.121-0.121-0.121-0.121-0.1030.512
TVOC[ppb]0.053-0.0161.0000.321-0.187-0.882-0.4500.1540.1510.1550.1510.1150.289
eCO2[ppm]0.165-0.1040.3211.000-0.716-0.315-0.2500.4340.4340.4340.4340.4260.121
Raw H2-0.2110.222-0.187-0.7161.0000.2010.372-0.293-0.297-0.293-0.298-0.3420.388
Raw Ethanol-0.034-0.136-0.882-0.3150.2011.0000.191-0.126-0.128-0.126-0.128-0.1270.680
Pressure[hPa]-0.2670.556-0.450-0.2500.3720.1911.000-0.176-0.174-0.176-0.173-0.1340.790
PM1.00.013-0.1210.1540.434-0.293-0.126-0.1761.0000.9981.0000.9980.9620.135
PM2.50.013-0.1210.1510.434-0.297-0.128-0.1740.9981.0000.9981.0000.9670.118
NC0.50.013-0.1210.1550.434-0.293-0.126-0.1761.0000.9981.0000.9970.9600.136
NC1.00.013-0.1210.1510.434-0.298-0.128-0.1730.9981.0000.9971.0000.9670.117
NC2.50.009-0.1030.1150.426-0.342-0.127-0.1340.9620.9670.9600.9671.0000.091
Fire Alarm0.3960.5120.2890.1210.3880.6800.7900.1350.1180.1360.1170.0911.000

Missing values

2023-05-09T00:04:17.457813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-09T00:04:17.672669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Temperature[C]Humidity[%]TVOC[ppb]eCO2[ppm]Raw H2Raw EthanolPressure[hPa]PM1.0PM2.5NC0.5NC1.0NC2.5Fire Alarm
00.2806420.994913-0.248616-0.14168-2.335897-2.0249900.831724-0.109044-0.093341-0.115216-0.091925-0.0738890
10.2816870.917083-0.248616-0.14168-2.192761-1.8100590.838484-0.109044-0.093341-0.115216-0.091925-0.0738890
20.2826620.836997-0.248616-0.14168-2.086326-1.6246600.833977-0.109044-0.093341-0.115216-0.091925-0.0738890
30.2837060.760295-0.248616-0.14168-2.027604-1.4852010.832475-0.109044-0.093341-0.115216-0.091925-0.0738890
40.2847510.693745-0.248616-0.14168-1.979892-1.3670710.838484-0.109044-0.093341-0.115216-0.091925-0.0738890
50.2857260.629450-0.248616-0.14168-1.921169-1.2407380.824213-0.109044-0.093341-0.115216-0.091925-0.0738890
60.2867700.571924-0.248616-0.14168-1.873457-1.1422960.833977-0.109044-0.093341-0.115216-0.091925-0.0738890
70.2878150.525677-0.248616-0.14168-1.847766-1.0504170.849000-0.109044-0.093341-0.115216-0.091925-0.0738890
80.2887900.481686-0.248616-0.14168-1.814735-0.9831480.849000-0.109044-0.093341-0.115216-0.091925-0.0738890
90.2898350.442207-0.248616-0.14168-1.796384-0.9175210.847497-0.108069-0.091429-0.115216-0.089952-0.0713230
Temperature[C]Humidity[%]TVOC[ppb]eCO2[ppm]Raw H2Raw EthanolPressure[hPa]PM1.0PM2.5NC0.5NC1.0NC2.5Fire Alarm
626200.097978-3.698571-0.169120-0.141682.8720541.338436-1.480963-0.108405-0.093033-0.114264-0.091639-0.0738760
626210.112672-3.700827-0.167328-0.141682.8463621.336796-1.471950-0.108350-0.093002-0.114180-0.091613-0.0738740
626220.127435-3.694059-0.167072-0.141682.8353521.305622-1.466692-0.108329-0.092997-0.114151-0.091606-0.0738740
626230.142199-3.695187-0.165408-0.141682.7913101.299060-1.468194-0.108329-0.092992-0.114144-0.091603-0.0738740
626240.157032-3.691803-0.167968-0.141682.8500331.328592-1.478710-0.108329-0.092997-0.114156-0.091607-0.0738740
626250.171865-3.694059-0.168608-0.141682.8647131.336796-1.470448-0.108361-0.093012-0.114203-0.091621-0.0738750
626260.186838-3.685035-0.170272-0.141682.8940741.367969-1.464439-0.108383-0.093022-0.114236-0.091631-0.0738750
626270.201741-3.688419-0.168352-0.141682.8720541.358125-1.457679-0.108426-0.093038-0.114290-0.091646-0.0738760
626280.216783-3.665860-0.166944-0.141682.8243421.331873-1.462937-0.108426-0.093043-0.114297-0.091649-0.0738760
626290.231825-3.611717-0.166304-0.141682.7656191.294137-1.465941-0.108426-0.093043-0.114301-0.091651-0.0738760